| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.6.0, < 2.6.3≥ 2.7.0, < 2.7.1< 2.5.3 | 2.6.32.7.12.5.3 |
| tensorflow-cpu | ≥ 2.6.0, < 2.6.3≥ 2.7.0, < 2.7.1< 2.5.3 | 2.6.32.7.12.5.3 |
| tensorflow-gpu | ≥ 2.6.0, < 2.6.3≥ 2.7.0, < 2.7.1< 2.5.3 | 2.6.32.7.12.5.3 |
| tensorflow | = 2.7.0≥ 2.6.0, ≤ 2.6.2≤ 2.5.2 |
The implementation of ThreadPoolHandle can be used to trigger a denial of service attack by allocating too much memory:
import tensorflow as tf
y = tf.raw_ops.ThreadPoolHandle(num_threads=0x60000000,display_name='tf')
This is because the num_threads argument is only checked to not be negative, but there is no upper bound on its value.
We have patched the issue in GitHub commit e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team.
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